ABSTRACT

This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These methods relax the linear assumption of many standard models and allow analysts to uncover structure in the data that might otherwise have been missed. While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range of data types, Generalized Additive Models enhances this methodology even further by incorporating the flexibility of nonparametric regression. Clear prose, exercises in each chapter, and case studies enhance this popular text.

chapter 1|8 pages

Introduction

chapter 2|30 pages

Smoothing

chapter 3|43 pages

Smoothing in detail

chapter 4|23 pages

Additive models

chapter 5|31 pages

Some theory for additive models

chapter 6|38 pages

Generalized additive models

chapter 7|27 pages

Response transformation models

chapter 8|34 pages

Extensions to other settings

chapter 9|46 pages

Further topics

chapter 10|20 pages

Case studies